Prediction of hypertension and restenosis under guideline-directed management in aortic coarctation: development and validation of machine-learning models
Lea Fierley, Jakob Versnjak, Grischa Gabel, Peter Kramer, Leonid Goubergrits, Felix Berger, Grégoire Montavon, Titus Kuehne, Marcus Kelm · 2026
Read Prediction of hypertension and restenosis under guideline-directed management in aortic coarctation: development and validation of machine-learning models by Lea Fierley, Jakob Versnjak, Grischa Gabel, Peter Kramer, Leonid Goubergrits, Felix Berger, Grégoire Montavon, Titus Kuehne, Marcus Kelm (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13320318.txt.
Description
Lea Fierley and co-authors train learning models to forecast high blood pressure and re narrowing in a pinched aorta in a 2026 study from Berlin. The birth defect narrows the main artery and stays a top cause of high pressure in the young. Return narrowing hits up to one third of people over a lifetime despite standard care. Early flags for who will relapse could sharpen follow up.
Records, heart scans, and long follow up from 218 visits by 160 people fed seven model types, tuned and split into training and hold out sets. Tree based and margin based models led the pack, sorting return cases with strong marks in the hold out set on the scale used. The models may help clinics aim scans and visits at those at most risk.
Catalog record
| CREATOR | Lea Fierley, Jakob Versnjak, Grischa Gabel, Peter Kramer, Leonid Goubergrits, Felix Berger, Grégoire Montavon, Titus Kuehne, Marcus Kelm |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 73950 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |